【作者】 张斌；

【导师】 程珩；

【作者基本信息】 太原理工大学 ， 机械电子工程， 2009， 硕士

【摘要】 机械设备的诊断过程包括诊断信息的获取、故障特征提取和状态识别三部分。其中故障特征提取和状态识别是故障诊断的关键。本文将时频分析的经验模态分解(EMD)和状态识别的支持向量机(SVM)相结合应用于齿轮箱故障诊断当中。EMD方法是基于信号局部特征时间尺度,能把复杂的信号函数分解为有限个固有模态函数(IMF)之和,而每个IMF表示了原始信号的一个固有振动模态,它们很好地体现了信号的局部特性。此外,由于每一个IMF所包含的频率成分不仅与采样频率有关,而且它还随着信号本身的变化而变化,因此EMD方法是一种自适应的时频局部化分析方法,非常适用于非平稳、非线性的信号处理过程。针对齿轮箱故障振动信号的非平稳特性,本文将EMD方法引入齿轮箱故障特征提取当中,对其基本理论进行研究,实现了用EMD方法提取信号故障特征。并对其端点效应产生失真问题,提出了一种新的解决方法,增强了EMD分解的精确度。支持向量机具有小样本、较好的泛化能力、全局最优解等特点,在状态识别领域中表现出优良的特性。针对在机械故障诊断中难以获得大量故障典型样本的实际情况,本文在基于支持向量机理论的基础上,开展了对齿轮箱的工作状态和故障类型进行分类和识别研究,并将其研究结果带入实验中,从数据的分析结果表明,EMD和SVM相结合可有效的应用于齿轮箱故障诊断当中。本文主要工作包括:1)从齿轮箱零部件常见的失效形式、齿轮系统的振动机理和齿轮箱典型故障振动信号的特征三个方面论述了有关齿轮箱故障诊断的基本知识。2)从分析EMD方法的基本理论入手,对EMD故障特征提取方法进行研究。其中重点分析了EMD的端点效应带来的影响,提出了处理端点效应的新方法,解决了EMD分解过程中产生失真的现象。3)从统计学习理论出发,基于支持向量机理论基础开展了状态识别的研究建立起相关的分类器及分类器的算法,验证了其算法的有效性。4)针对齿轮箱常见的故障,进行故障模拟实验,并应用EMD提取故障信号特征,采用SVM分类器对齿轮箱的工作状态和故障类型进行分类、识别,得到较好的效果。更多还原

【Abstract】 The process of machinery fault diagnosis includes the acquisition of information and extracting feature and recognizing conditions of which feature extraction and condition identification are the priority. A method of time-frequency analysis, Empirical Mode Decomposition (EMD) and the comparatively recent development of pattern recognition techniques, Support Vector Machines (SVM), are combined and applied to the gearbox fault diagnosis.EMD is based on the local characteristic time scale of signal and decompose the complicated signal into a number of Intrinsic Mode Functions (IMF). Involving the local characteristic of the signal, each IMF component expresses the original signal of an inherent vibration mode. In addition, the frequency components involved in each IMF not only relates to sampling frequency but also changes with the signal itself, therefore, EMD is a self-adaptive time frequency analysis method that is applicable to non-linear and not-stationary processes perfectly. According to the non-stationary vibration signal characteristics of gearbox, EMD method is introduced into gearbox fault diagnosis in the paper. The basic theory of EMD is studied to extract the signal fault feature. And a new method dealing with the end effect of EMD is proposed to enhance the accuracy of EMD decomposition.SVM has better generalization and guarantee the local optimal solution is exactly the global optimal solution. SVM can solve the learning problem of a smaller number of samples. Due to the fact that it is difficult to obtain sufficient fault samples in practice, SVM is introduced into gearbox fault diagnosis due to their high accuracy and good generalization for a smaller sample number. Based on the theory of SVM, the work on the gear box status and fault type classification and identification is studied in the paper. And it’s findings into the experiment. The experimental results demonstrate the proposed diagnosis approach in which EMD and SVM are combined is effective.The outline of the work is as follows:1. The common parts of gearbox failure, gearbox vibration mechanism and the typical failure characteristics of vibration signals are introduced.2. The fault feature extraction from the analysis of the EMD basic theory is studied, and the impact of the end effect of EMD is analyzed. A new method dealing with the end effect of EMD is proposed, and it solved the signal distortion during the process of EMD.3. The status of the classifier to establish relevant and classifier algorithms,and verifies the validity of the algorithm is researched from the statistical learning theory. 4. Simulation experiments of gear box common fault were done to extract the feature of signal with EMD. SVM classifier was used on the gear box of the type of job status and fault classification, and gets a better effect.更多还原